{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:52:34.899431Z",
     "start_time": "2020-11-21T02:52:34.763367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总用量 36520\n",
      "-rw-r--r-- 1 root root  2782366 10月 13 09:12 annual_report_info.csv\n",
      "-rw-r--r-- 1 root root 15579016 10月 15 17:11 base_info.csv\n",
      "-rw-r--r-- 1 root root 13523812 10月 13 09:14 change_info.csv\n",
      "-rw-r--r-- 1 root root   772990 10月 13 09:12 entprise_info.csv\n",
      "-rw-r--r-- 1 root root   715262 10月 13 09:12 news_info.csv\n",
      "-rw-r--r-- 1 root root   107895 10月 13 09:12 other_info.csv\n",
      "-rw-r--r-- 1 root root  3902809 10月 13 09:12 tax_info.csv\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "!ls ../input/train/ -l\n",
    "\n",
    "PATH = '../input/train/'\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "pd.set_option('max.columns', 100)\n",
    "\n",
    "import missingno as msno\n",
    "\n",
    "%pylab inline\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "from tqdm import tqdm\n",
    "\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import catboost as cab\n",
    "\n",
    "from sklearn.metrics import classification_report, f1_score\n",
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score,precision_recall_fscore_support,roc_curve,auc,roc_auc_score\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 赛题介绍\n",
    "\n",
    "- `赛题名`：企业非法集资风险预测\n",
    "\n",
    "- `背景`：非法集资严重干扰了正常的经济、金融秩序，使参与者遭受经济损失，甚至生活陷入困境，极易引发社会不稳定和大量社会治安问题，甚至引发局部地区的社会动荡。如何根据大量的企业信息建立预测模型并判断企业是否存在非法集资风险，对监管部门、企业合作伙伴、投资者都具有一定的价值。\n",
    "\n",
    "- `任务`：利用机器学习、深度学习等方法训练一个预测模型，该模型可学习企业的相关信息，**以预测企业是否存在非法集资风险**。赛题的难点在于数据集包括大量的企业相关信息，如何从中提取有效的特征并进行风险预测成为本赛题的关键问题。\n",
    "\n",
    "- `出题单位`：中科大智慧城市研究院\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据读取&分析\n",
    "\n",
    "该数据集包含约25000家企业数据，其中约15000家企业带标注数据作为训练集，剩余数据作为测试集。数据由企业基本信息、企业年报、企业纳税情况等组成，数据包括数值型、字符型、日期型等众多数据类型（已脱敏），部分字段内容在部分企业中有缺失，其中第一列id为企业唯一标识。\n",
    "\n",
    "## 数据说明\n",
    "\n",
    "### 数据集1：base_info.csv\n",
    "\n",
    "包含数据集7和8中涉及到的所有企业的基本信息，每一行代表一个企业的基本数据，每一行有33列，其中id列为企业唯一标识，列之间采用“,”分隔符分割。\n",
    "\n",
    "数据格式如下：\n",
    "\n",
    "|   列名   |  含义    |   列名   |   含义   |   列名   |  含义    |   列名   |   含义   | \n",
    "| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n",
    "| id | 企业唯一标识 | oplocdistrict | 行政区划代码 | industryphy | 行业类别代码 | industryco | 行业细类代码 |\n",
    "| dom | 经营地址 | opscope | 经营范围 | enttype | 企业类型 | enttypeitem | 企业类型小类 |\n",
    "| opfrom | 经营期限起 | opto | 经营期限止 | state | 状态 | orgid | 机构标识 |\n",
    "| jobid | 职位标识 | adbusign | 是否广告经营 | townsign | 是否城镇 | regtype | 主题登记类型 |\n",
    "| empnum | 从业人数 | compform | 组织形式 | parnum | 合伙人数 | exenum | 执行人数 |\n",
    "| opform | 经营方式 | ptbusscope | 兼营范围 | venind | 风险行业 | enttypeminu | 企业类型细类 |\n",
    "| midpreindcode | 中西部优势产业代码 | protype | 项目类型 | oploc | 经营场所 | regcap | 注册资本（金） |\n",
    "| reccap | 实缴资本 | forreccap | 实缴资本（外方） | forregcap | 注册资本（外方） | congro | 投资总额 |\n",
    "| enttypegb | 企业（机构）类型 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:45:15.253058Z",
     "start_time": "2020-11-21T02:45:14.724215Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24865, 33) 24865\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>oplocdistrict</th>\n",
       "      <th>industryphy</th>\n",
       "      <th>industryco</th>\n",
       "      <th>dom</th>\n",
       "      <th>opscope</th>\n",
       "      <th>enttype</th>\n",
       "      <th>enttypeitem</th>\n",
       "      <th>opfrom</th>\n",
       "      <th>opto</th>\n",
       "      <th>state</th>\n",
       "      <th>orgid</th>\n",
       "      <th>jobid</th>\n",
       "      <th>adbusign</th>\n",
       "      <th>townsign</th>\n",
       "      <th>regtype</th>\n",
       "      <th>empnum</th>\n",
       "      <th>compform</th>\n",
       "      <th>parnum</th>\n",
       "      <th>exenum</th>\n",
       "      <th>opform</th>\n",
       "      <th>ptbusscope</th>\n",
       "      <th>venind</th>\n",
       "      <th>enttypeminu</th>\n",
       "      <th>midpreindcode</th>\n",
       "      <th>protype</th>\n",
       "      <th>oploc</th>\n",
       "      <th>regcap</th>\n",
       "      <th>reccap</th>\n",
       "      <th>forreccap</th>\n",
       "      <th>forregcap</th>\n",
       "      <th>congro</th>\n",
       "      <th>enttypegb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47645761dc56bb8c5fae00114b768b5d9b6e917c3aec07c4</td>\n",
       "      <td>340223</td>\n",
       "      <td>M</td>\n",
       "      <td>7513.0</td>\n",
       "      <td>31487d8f256f16bd6244b7251be2ebb24d1db51663c654...</td>\n",
       "      <td>纳米新材料、机械设备、五金配件加工、销售及技术推广服务，道路货物运输。（依法须经批准的项目，...</td>\n",
       "      <td>1100</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>2019-07-11 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>340223010010000000</td>\n",
       "      <td>340200000000115392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2367b4cac96d8598</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 id  oplocdistrict  \\\n",
       "0  47645761dc56bb8c5fae00114b768b5d9b6e917c3aec07c4         340223   \n",
       "\n",
       "  industryphy  industryco                                                dom  \\\n",
       "0           M      7513.0  31487d8f256f16bd6244b7251be2ebb24d1db51663c654...   \n",
       "\n",
       "                                             opscope  enttype  enttypeitem  \\\n",
       "0  纳米新材料、机械设备、五金配件加工、销售及技术推广服务，道路货物运输。（依法须经批准的项目，...     1100       1150.0   \n",
       "\n",
       "                opfrom opto  state               orgid               jobid  \\\n",
       "0  2019-07-11 00:00:00  NaN      6  340223010010000000  340200000000115392   \n",
       "\n",
       "   adbusign  townsign  regtype  empnum  compform  parnum  exenum opform  \\\n",
       "0         0         0        1     5.0       NaN     NaN     NaN    NaN   \n",
       "\n",
       "   ptbusscope  venind  enttypeminu  midpreindcode  protype             oploc  \\\n",
       "0         NaN     NaN       1151.0            NaN      NaN  2367b4cac96d8598   \n",
       "\n",
       "   regcap  reccap  forreccap  forregcap  congro  enttypegb  \n",
       "0    50.0     NaN        NaN        NaN     NaN       1151  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_info = pd.read_csv(PATH + 'base_info.csv')\n",
    "\n",
    "print(base_info.shape, base_info['id'].nunique())\n",
    "base_info.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:45:17.072047Z",
     "start_time": "2020-11-21T02:45:15.254675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 缺失值\n",
    "msno.bar(base_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:45:17.126586Z",
     "start_time": "2020-11-21T02:45:17.073349Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id 24865\n",
      "oplocdistrict 16\n",
      "industryphy 20\n",
      "industryco 345\n",
      "dom 23278\n",
      "opscope 20815\n",
      "enttype 17\n",
      "enttypeitem 31\n",
      "opfrom 6620\n",
      "opto 5746\n",
      "state 6\n",
      "orgid 78\n",
      "jobid 434\n",
      "adbusign 2\n",
      "townsign 2\n",
      "regtype 3\n",
      "empnum 62\n",
      "compform 2\n",
      "parnum 51\n",
      "exenum 50\n",
      "opform 33\n",
      "ptbusscope 0\n",
      "venind 3\n",
      "enttypeminu 26\n",
      "midpreindcode 0\n",
      "protype 2\n",
      "oploc 5351\n",
      "regcap 1143\n",
      "reccap 597\n",
      "forreccap 11\n",
      "forregcap 38\n",
      "congro 33\n",
      "enttypegb 53\n"
     ]
    }
   ],
   "source": [
    "for col in base_info.columns:\n",
    "    print(col, base_info[col].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:45:17.220201Z",
     "start_time": "2020-11-21T02:45:17.127650Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oplocdistrict</th>\n",
       "      <th>industryco</th>\n",
       "      <th>enttype</th>\n",
       "      <th>enttypeitem</th>\n",
       "      <th>state</th>\n",
       "      <th>orgid</th>\n",
       "      <th>jobid</th>\n",
       "      <th>adbusign</th>\n",
       "      <th>townsign</th>\n",
       "      <th>regtype</th>\n",
       "      <th>empnum</th>\n",
       "      <th>compform</th>\n",
       "      <th>parnum</th>\n",
       "      <th>exenum</th>\n",
       "      <th>ptbusscope</th>\n",
       "      <th>venind</th>\n",
       "      <th>enttypeminu</th>\n",
       "      <th>midpreindcode</th>\n",
       "      <th>protype</th>\n",
       "      <th>regcap</th>\n",
       "      <th>reccap</th>\n",
       "      <th>forreccap</th>\n",
       "      <th>forregcap</th>\n",
       "      <th>congro</th>\n",
       "      <th>enttypegb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>oplocdistrict</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.112593</td>\n",
       "      <td>0.121533</td>\n",
       "      <td>-0.041273</td>\n",
       "      <td>0.019492</td>\n",
       "      <td>-0.020198</td>\n",
       "      <td>0.078328</td>\n",
       "      <td>-0.008561</td>\n",
       "      <td>-0.033043</td>\n",
       "      <td>-0.042385</td>\n",
       "      <td>0.024939</td>\n",
       "      <td>0.028629</td>\n",
       "      <td>-0.073539</td>\n",
       "      <td>-0.008300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.081515</td>\n",
       "      <td>-0.176448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005271</td>\n",
       "      <td>-0.041067</td>\n",
       "      <td>-0.042661</td>\n",
       "      <td>-0.104363</td>\n",
       "      <td>-0.077224</td>\n",
       "      <td>-0.078097</td>\n",
       "      <td>0.121500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industryco</th>\n",
       "      <td>0.112593</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>-0.094717</td>\n",
       "      <td>0.030038</td>\n",
       "      <td>0.056266</td>\n",
       "      <td>0.048277</td>\n",
       "      <td>-0.002085</td>\n",
       "      <td>-0.058483</td>\n",
       "      <td>-0.051295</td>\n",
       "      <td>-0.044818</td>\n",
       "      <td>0.008104</td>\n",
       "      <td>-0.214439</td>\n",
       "      <td>-0.018191</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.068183</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.111578</td>\n",
       "      <td>-0.106896</td>\n",
       "      <td>-0.090893</td>\n",
       "      <td>-0.372025</td>\n",
       "      <td>-0.279505</td>\n",
       "      <td>-0.308839</td>\n",
       "      <td>0.257281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enttype</th>\n",
       "      <td>0.121533</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>-0.027878</td>\n",
       "      <td>0.179735</td>\n",
       "      <td>-0.178680</td>\n",
       "      <td>-0.042500</td>\n",
       "      <td>0.346220</td>\n",
       "      <td>-0.097321</td>\n",
       "      <td>-0.034183</td>\n",
       "      <td>0.023962</td>\n",
       "      <td>0.261070</td>\n",
       "      <td>-0.000758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.275222</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.086552</td>\n",
       "      <td>-0.008691</td>\n",
       "      <td>0.094448</td>\n",
       "      <td>0.212679</td>\n",
       "      <td>0.184072</td>\n",
       "      <td>0.184857</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enttypeitem</th>\n",
       "      <td>-0.041273</td>\n",
       "      <td>-0.094717</td>\n",
       "      <td>0.999932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010287</td>\n",
       "      <td>0.026695</td>\n",
       "      <td>-0.166773</td>\n",
       "      <td>-0.025031</td>\n",
       "      <td>0.224193</td>\n",
       "      <td>-0.032974</td>\n",
       "      <td>0.021419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.260897</td>\n",
       "      <td>-0.000758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.162243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.082266</td>\n",
       "      <td>0.158789</td>\n",
       "      <td>0.100358</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.317294</td>\n",
       "      <td>0.321941</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0.019492</td>\n",
       "      <td>0.030038</td>\n",
       "      <td>-0.027878</td>\n",
       "      <td>-0.010287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010450</td>\n",
       "      <td>-0.025917</td>\n",
       "      <td>0.005841</td>\n",
       "      <td>0.055396</td>\n",
       "      <td>-0.001333</td>\n",
       "      <td>-0.009471</td>\n",
       "      <td>0.016457</td>\n",
       "      <td>-0.012364</td>\n",
       "      <td>-0.004302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.022642</td>\n",
       "      <td>-0.083751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.144005</td>\n",
       "      <td>-0.016389</td>\n",
       "      <td>-0.029711</td>\n",
       "      <td>-0.046624</td>\n",
       "      <td>-0.041558</td>\n",
       "      <td>-0.043007</td>\n",
       "      <td>-0.027873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orgid</th>\n",
       "      <td>-0.020198</td>\n",
       "      <td>0.056266</td>\n",
       "      <td>0.179735</td>\n",
       "      <td>0.026695</td>\n",
       "      <td>-0.010450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100055</td>\n",
       "      <td>-0.007011</td>\n",
       "      <td>0.059033</td>\n",
       "      <td>-0.014396</td>\n",
       "      <td>-0.012030</td>\n",
       "      <td>0.014091</td>\n",
       "      <td>-0.059041</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.023426</td>\n",
       "      <td>-0.134803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.102869</td>\n",
       "      <td>-0.010051</td>\n",
       "      <td>-0.001102</td>\n",
       "      <td>0.003824</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.003553</td>\n",
       "      <td>0.179710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jobid</th>\n",
       "      <td>0.078328</td>\n",
       "      <td>0.048277</td>\n",
       "      <td>-0.178680</td>\n",
       "      <td>-0.166773</td>\n",
       "      <td>-0.025917</td>\n",
       "      <td>0.100055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022339</td>\n",
       "      <td>-0.274408</td>\n",
       "      <td>-0.013339</td>\n",
       "      <td>0.018536</td>\n",
       "      <td>-0.007621</td>\n",
       "      <td>-0.246189</td>\n",
       "      <td>-0.008265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.009625</td>\n",
       "      <td>-0.245806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.080584</td>\n",
       "      <td>-0.028447</td>\n",
       "      <td>-0.017728</td>\n",
       "      <td>-0.019219</td>\n",
       "      <td>0.000662</td>\n",
       "      <td>0.013314</td>\n",
       "      <td>-0.178719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adbusign</th>\n",
       "      <td>-0.008561</td>\n",
       "      <td>-0.002085</td>\n",
       "      <td>-0.042500</td>\n",
       "      <td>-0.025031</td>\n",
       "      <td>0.005841</td>\n",
       "      <td>-0.007011</td>\n",
       "      <td>-0.022339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016126</td>\n",
       "      <td>-0.003955</td>\n",
       "      <td>-0.013402</td>\n",
       "      <td>-0.003266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.021901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000085</td>\n",
       "      <td>0.002786</td>\n",
       "      <td>-0.032364</td>\n",
       "      <td>-0.028940</td>\n",
       "      <td>-0.030020</td>\n",
       "      <td>-0.042538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>townsign</th>\n",
       "      <td>-0.033043</td>\n",
       "      <td>-0.058483</td>\n",
       "      <td>0.346220</td>\n",
       "      <td>0.224193</td>\n",
       "      <td>0.055396</td>\n",
       "      <td>0.059033</td>\n",
       "      <td>-0.274408</td>\n",
       "      <td>-0.016126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.072162</td>\n",
       "      <td>-0.028617</td>\n",
       "      <td>0.007102</td>\n",
       "      <td>0.253791</td>\n",
       "      <td>0.012175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010278</td>\n",
       "      <td>0.372428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039094</td>\n",
       "      <td>0.027127</td>\n",
       "      <td>-0.077001</td>\n",
       "      <td>-0.068255</td>\n",
       "      <td>-0.070828</td>\n",
       "      <td>0.346336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regtype</th>\n",
       "      <td>-0.042385</td>\n",
       "      <td>-0.051295</td>\n",
       "      <td>-0.097321</td>\n",
       "      <td>-0.032974</td>\n",
       "      <td>-0.001333</td>\n",
       "      <td>-0.014396</td>\n",
       "      <td>-0.013339</td>\n",
       "      <td>-0.003955</td>\n",
       "      <td>-0.072162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002866</td>\n",
       "      <td>0.008554</td>\n",
       "      <td>0.063020</td>\n",
       "      <td>-0.003083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067663</td>\n",
       "      <td>-0.068709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.424139</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>0.022605</td>\n",
       "      <td>0.083947</td>\n",
       "      <td>0.142100</td>\n",
       "      <td>0.142878</td>\n",
       "      <td>-0.097343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empnum</th>\n",
       "      <td>0.024939</td>\n",
       "      <td>-0.044818</td>\n",
       "      <td>-0.034183</td>\n",
       "      <td>0.021419</td>\n",
       "      <td>-0.009471</td>\n",
       "      <td>-0.012030</td>\n",
       "      <td>0.018536</td>\n",
       "      <td>-0.013402</td>\n",
       "      <td>-0.028617</td>\n",
       "      <td>0.002866</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>0.160443</td>\n",
       "      <td>-0.015736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.013970</td>\n",
       "      <td>0.014231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.249365</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.014895</td>\n",
       "      <td>0.116721</td>\n",
       "      <td>0.165063</td>\n",
       "      <td>0.134318</td>\n",
       "      <td>-0.034006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compform</th>\n",
       "      <td>0.028629</td>\n",
       "      <td>0.008104</td>\n",
       "      <td>0.023962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016457</td>\n",
       "      <td>0.014091</td>\n",
       "      <td>-0.007621</td>\n",
       "      <td>-0.003266</td>\n",
       "      <td>0.007102</td>\n",
       "      <td>0.008554</td>\n",
       "      <td>0.007968</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.065711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parnum</th>\n",
       "      <td>-0.073539</td>\n",
       "      <td>-0.214439</td>\n",
       "      <td>0.261070</td>\n",
       "      <td>0.260897</td>\n",
       "      <td>-0.012364</td>\n",
       "      <td>-0.059041</td>\n",
       "      <td>-0.246189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.253791</td>\n",
       "      <td>0.063020</td>\n",
       "      <td>0.160443</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.236992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.349526</td>\n",
       "      <td>0.227412</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012390</td>\n",
       "      <td>0.006806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.260902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exenum</th>\n",
       "      <td>-0.008300</td>\n",
       "      <td>-0.018191</td>\n",
       "      <td>-0.000758</td>\n",
       "      <td>-0.000758</td>\n",
       "      <td>-0.004302</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>-0.008265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012175</td>\n",
       "      <td>-0.003083</td>\n",
       "      <td>-0.015736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.236992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.461009</td>\n",
       "      <td>0.004403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>-0.006589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ptbusscope</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>venind</th>\n",
       "      <td>-0.081515</td>\n",
       "      <td>-0.068183</td>\n",
       "      <td>0.275222</td>\n",
       "      <td>0.162243</td>\n",
       "      <td>-0.022642</td>\n",
       "      <td>-0.023426</td>\n",
       "      <td>-0.009625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010278</td>\n",
       "      <td>-0.067663</td>\n",
       "      <td>-0.013970</td>\n",
       "      <td>-0.065711</td>\n",
       "      <td>0.349526</td>\n",
       "      <td>0.461009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.401748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.086325</td>\n",
       "      <td>-0.000793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.275019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enttypeminu</th>\n",
       "      <td>-0.176448</td>\n",
       "      <td>-0.461538</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.083751</td>\n",
       "      <td>-0.134803</td>\n",
       "      <td>-0.245806</td>\n",
       "      <td>-0.021901</td>\n",
       "      <td>0.372428</td>\n",
       "      <td>-0.068709</td>\n",
       "      <td>0.014231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.227412</td>\n",
       "      <td>0.004403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.401748</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.228271</td>\n",
       "      <td>0.145498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>midpreindcode</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protype</th>\n",
       "      <td>0.005271</td>\n",
       "      <td>-0.111578</td>\n",
       "      <td>-0.086552</td>\n",
       "      <td>-0.082266</td>\n",
       "      <td>0.144005</td>\n",
       "      <td>0.102869</td>\n",
       "      <td>-0.080584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.424139</td>\n",
       "      <td>-0.249365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071690</td>\n",
       "      <td>0.141604</td>\n",
       "      <td>0.173116</td>\n",
       "      <td>0.156798</td>\n",
       "      <td>0.147581</td>\n",
       "      <td>-0.082259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regcap</th>\n",
       "      <td>-0.041067</td>\n",
       "      <td>-0.106896</td>\n",
       "      <td>-0.008691</td>\n",
       "      <td>0.158789</td>\n",
       "      <td>-0.016389</td>\n",
       "      <td>-0.010051</td>\n",
       "      <td>-0.028447</td>\n",
       "      <td>-0.000085</td>\n",
       "      <td>0.039094</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>-0.001599</td>\n",
       "      <td>0.012390</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.086325</td>\n",
       "      <td>0.228271</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071690</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464144</td>\n",
       "      <td>-0.018039</td>\n",
       "      <td>-0.017124</td>\n",
       "      <td>-0.012161</td>\n",
       "      <td>-0.008523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reccap</th>\n",
       "      <td>-0.042661</td>\n",
       "      <td>-0.090893</td>\n",
       "      <td>0.094448</td>\n",
       "      <td>0.100358</td>\n",
       "      <td>-0.029711</td>\n",
       "      <td>-0.001102</td>\n",
       "      <td>-0.017728</td>\n",
       "      <td>0.002786</td>\n",
       "      <td>0.027127</td>\n",
       "      <td>0.022605</td>\n",
       "      <td>0.014895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006806</td>\n",
       "      <td>-0.006589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000793</td>\n",
       "      <td>0.145498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.141604</td>\n",
       "      <td>0.464144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022261</td>\n",
       "      <td>-0.027902</td>\n",
       "      <td>-0.025398</td>\n",
       "      <td>0.095021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forreccap</th>\n",
       "      <td>-0.104363</td>\n",
       "      <td>-0.372025</td>\n",
       "      <td>0.212679</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>-0.046624</td>\n",
       "      <td>0.003824</td>\n",
       "      <td>-0.019219</td>\n",
       "      <td>-0.032364</td>\n",
       "      <td>-0.077001</td>\n",
       "      <td>0.083947</td>\n",
       "      <td>0.116721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.173116</td>\n",
       "      <td>-0.018039</td>\n",
       "      <td>-0.022261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.650067</td>\n",
       "      <td>0.704133</td>\n",
       "      <td>0.214113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forregcap</th>\n",
       "      <td>-0.077224</td>\n",
       "      <td>-0.279505</td>\n",
       "      <td>0.184072</td>\n",
       "      <td>0.317294</td>\n",
       "      <td>-0.041558</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.000662</td>\n",
       "      <td>-0.028940</td>\n",
       "      <td>-0.068255</td>\n",
       "      <td>0.142100</td>\n",
       "      <td>0.165063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.156798</td>\n",
       "      <td>-0.017124</td>\n",
       "      <td>-0.027902</td>\n",
       "      <td>0.650067</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964826</td>\n",
       "      <td>0.185152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>congro</th>\n",
       "      <td>-0.078097</td>\n",
       "      <td>-0.308839</td>\n",
       "      <td>0.184857</td>\n",
       "      <td>0.321941</td>\n",
       "      <td>-0.043007</td>\n",
       "      <td>0.003553</td>\n",
       "      <td>0.013314</td>\n",
       "      <td>-0.030020</td>\n",
       "      <td>-0.070828</td>\n",
       "      <td>0.142878</td>\n",
       "      <td>0.134318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.147581</td>\n",
       "      <td>-0.012161</td>\n",
       "      <td>-0.025398</td>\n",
       "      <td>0.704133</td>\n",
       "      <td>0.964826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.185857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enttypegb</th>\n",
       "      <td>0.121500</td>\n",
       "      <td>0.257281</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027873</td>\n",
       "      <td>0.179710</td>\n",
       "      <td>-0.178719</td>\n",
       "      <td>-0.042538</td>\n",
       "      <td>0.346336</td>\n",
       "      <td>-0.097343</td>\n",
       "      <td>-0.034006</td>\n",
       "      <td>0.023961</td>\n",
       "      <td>0.260902</td>\n",
       "      <td>-0.000703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.275019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.082259</td>\n",
       "      <td>-0.008523</td>\n",
       "      <td>0.095021</td>\n",
       "      <td>0.214113</td>\n",
       "      <td>0.185152</td>\n",
       "      <td>0.185857</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oplocdistrict  industryco   enttype  enttypeitem     state  \\\n",
       "oplocdistrict       1.000000    0.112593  0.121533    -0.041273  0.019492   \n",
       "industryco          0.112593    1.000000  0.257469    -0.094717  0.030038   \n",
       "enttype             0.121533    0.257469  1.000000     0.999932 -0.027878   \n",
       "enttypeitem        -0.041273   -0.094717  0.999932     1.000000 -0.010287   \n",
       "state               0.019492    0.030038 -0.027878    -0.010287  1.000000   \n",
       "orgid              -0.020198    0.056266  0.179735     0.026695 -0.010450   \n",
       "jobid               0.078328    0.048277 -0.178680    -0.166773 -0.025917   \n",
       "adbusign           -0.008561   -0.002085 -0.042500    -0.025031  0.005841   \n",
       "townsign           -0.033043   -0.058483  0.346220     0.224193  0.055396   \n",
       "regtype            -0.042385   -0.051295 -0.097321    -0.032974 -0.001333   \n",
       "empnum              0.024939   -0.044818 -0.034183     0.021419 -0.009471   \n",
       "compform            0.028629    0.008104  0.023962          NaN  0.016457   \n",
       "parnum             -0.073539   -0.214439  0.261070     0.260897 -0.012364   \n",
       "exenum             -0.008300   -0.018191 -0.000758    -0.000758 -0.004302   \n",
       "ptbusscope               NaN         NaN       NaN          NaN       NaN   \n",
       "venind             -0.081515   -0.068183  0.275222     0.162243 -0.022642   \n",
       "enttypeminu        -0.176448   -0.461538  0.999996     1.000000 -0.083751   \n",
       "midpreindcode            NaN         NaN       NaN          NaN       NaN   \n",
       "protype             0.005271   -0.111578 -0.086552    -0.082266  0.144005   \n",
       "regcap             -0.041067   -0.106896 -0.008691     0.158789 -0.016389   \n",
       "reccap             -0.042661   -0.090893  0.094448     0.100358 -0.029711   \n",
       "forreccap          -0.104363   -0.372025  0.212679     0.454545 -0.046624   \n",
       "forregcap          -0.077224   -0.279505  0.184072     0.317294 -0.041558   \n",
       "congro             -0.078097   -0.308839  0.184857     0.321941 -0.043007   \n",
       "enttypegb           0.121500    0.257281  0.999995     1.000000 -0.027873   \n",
       "\n",
       "                  orgid     jobid  adbusign  townsign   regtype    empnum  \\\n",
       "oplocdistrict -0.020198  0.078328 -0.008561 -0.033043 -0.042385  0.024939   \n",
       "industryco     0.056266  0.048277 -0.002085 -0.058483 -0.051295 -0.044818   \n",
       "enttype        0.179735 -0.178680 -0.042500  0.346220 -0.097321 -0.034183   \n",
       "enttypeitem    0.026695 -0.166773 -0.025031  0.224193 -0.032974  0.021419   \n",
       "state         -0.010450 -0.025917  0.005841  0.055396 -0.001333 -0.009471   \n",
       "orgid          1.000000  0.100055 -0.007011  0.059033 -0.014396 -0.012030   \n",
       "jobid          0.100055  1.000000 -0.022339 -0.274408 -0.013339  0.018536   \n",
       "adbusign      -0.007011 -0.022339  1.000000 -0.016126 -0.003955 -0.013402   \n",
       "townsign       0.059033 -0.274408 -0.016126  1.000000 -0.072162 -0.028617   \n",
       "regtype       -0.014396 -0.013339 -0.003955 -0.072162  1.000000  0.002866   \n",
       "empnum        -0.012030  0.018536 -0.013402 -0.028617  0.002866  1.000000   \n",
       "compform       0.014091 -0.007621 -0.003266  0.007102  0.008554  0.007968   \n",
       "parnum        -0.059041 -0.246189       NaN  0.253791  0.063020  0.160443   \n",
       "exenum        -0.008210 -0.008265       NaN  0.012175 -0.003083 -0.015736   \n",
       "ptbusscope          NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "venind        -0.023426 -0.009625       NaN  0.010278 -0.067663 -0.013970   \n",
       "enttypeminu   -0.134803 -0.245806 -0.021901  0.372428 -0.068709  0.014231   \n",
       "midpreindcode       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "protype        0.102869 -0.080584       NaN       NaN -0.424139 -0.249365   \n",
       "regcap        -0.010051 -0.028447 -0.000085  0.039094  0.002011  0.000847   \n",
       "reccap        -0.001102 -0.017728  0.002786  0.027127  0.022605  0.014895   \n",
       "forreccap      0.003824 -0.019219 -0.032364 -0.077001  0.083947  0.116721   \n",
       "forregcap      0.003426  0.000662 -0.028940 -0.068255  0.142100  0.165063   \n",
       "congro         0.003553  0.013314 -0.030020 -0.070828  0.142878  0.134318   \n",
       "enttypegb      0.179710 -0.178719 -0.042538  0.346336 -0.097343 -0.034006   \n",
       "\n",
       "               compform    parnum    exenum  ptbusscope    venind  \\\n",
       "oplocdistrict  0.028629 -0.073539 -0.008300         NaN -0.081515   \n",
       "industryco     0.008104 -0.214439 -0.018191         NaN -0.068183   \n",
       "enttype        0.023962  0.261070 -0.000758         NaN  0.275222   \n",
       "enttypeitem         NaN  0.260897 -0.000758         NaN  0.162243   \n",
       "state          0.016457 -0.012364 -0.004302         NaN -0.022642   \n",
       "orgid          0.014091 -0.059041 -0.008210         NaN -0.023426   \n",
       "jobid         -0.007621 -0.246189 -0.008265         NaN -0.009625   \n",
       "adbusign      -0.003266       NaN       NaN         NaN       NaN   \n",
       "townsign       0.007102  0.253791  0.012175         NaN  0.010278   \n",
       "regtype        0.008554  0.063020 -0.003083         NaN -0.067663   \n",
       "empnum         0.007968  0.160443 -0.015736         NaN -0.013970   \n",
       "compform       1.000000       NaN       NaN         NaN -0.065711   \n",
       "parnum              NaN  1.000000  0.236992         NaN  0.349526   \n",
       "exenum              NaN  0.236992  1.000000         NaN  0.461009   \n",
       "ptbusscope          NaN       NaN       NaN         NaN       NaN   \n",
       "venind        -0.065711  0.349526  0.461009         NaN  1.000000   \n",
       "enttypeminu         NaN  0.227412  0.004403         NaN -0.401748   \n",
       "midpreindcode       NaN       NaN       NaN         NaN       NaN   \n",
       "protype             NaN       NaN       NaN         NaN       NaN   \n",
       "regcap        -0.001599  0.012390  0.002717         NaN -0.086325   \n",
       "reccap              NaN  0.006806 -0.006589         NaN -0.000793   \n",
       "forreccap           NaN       NaN       NaN         NaN       NaN   \n",
       "forregcap           NaN  1.000000       NaN         NaN       NaN   \n",
       "congro              NaN       NaN       NaN         NaN       NaN   \n",
       "enttypegb      0.023961  0.260902 -0.000703         NaN  0.275019   \n",
       "\n",
       "               enttypeminu  midpreindcode   protype    regcap    reccap  \\\n",
       "oplocdistrict    -0.176448            NaN  0.005271 -0.041067 -0.042661   \n",
       "industryco       -0.461538            NaN -0.111578 -0.106896 -0.090893   \n",
       "enttype           0.999996            NaN -0.086552 -0.008691  0.094448   \n",
       "enttypeitem       1.000000            NaN -0.082266  0.158789  0.100358   \n",
       "state            -0.083751            NaN  0.144005 -0.016389 -0.029711   \n",
       "orgid            -0.134803            NaN  0.102869 -0.010051 -0.001102   \n",
       "jobid            -0.245806            NaN -0.080584 -0.028447 -0.017728   \n",
       "adbusign         -0.021901            NaN       NaN -0.000085  0.002786   \n",
       "townsign          0.372428            NaN       NaN  0.039094  0.027127   \n",
       "regtype          -0.068709            NaN -0.424139  0.002011  0.022605   \n",
       "empnum            0.014231            NaN -0.249365  0.000847  0.014895   \n",
       "compform               NaN            NaN       NaN -0.001599       NaN   \n",
       "parnum            0.227412            NaN       NaN  0.012390  0.006806   \n",
       "exenum            0.004403            NaN       NaN  0.002717 -0.006589   \n",
       "ptbusscope             NaN            NaN       NaN       NaN       NaN   \n",
       "venind           -0.401748            NaN       NaN -0.086325 -0.000793   \n",
       "enttypeminu       1.000000            NaN       NaN  0.228271  0.145498   \n",
       "midpreindcode          NaN            NaN       NaN       NaN       NaN   \n",
       "protype                NaN            NaN  1.000000  0.071690  0.141604   \n",
       "regcap            0.228271            NaN  0.071690  1.000000  0.464144   \n",
       "reccap            0.145498            NaN  0.141604  0.464144  1.000000   \n",
       "forreccap              NaN            NaN  0.173116 -0.018039 -0.022261   \n",
       "forregcap              NaN            NaN  0.156798 -0.017124 -0.027902   \n",
       "congro                 NaN            NaN  0.147581 -0.012161 -0.025398   \n",
       "enttypegb         1.000000            NaN -0.082259 -0.008523  0.095021   \n",
       "\n",
       "               forreccap  forregcap    congro  enttypegb  \n",
       "oplocdistrict  -0.104363  -0.077224 -0.078097   0.121500  \n",
       "industryco     -0.372025  -0.279505 -0.308839   0.257281  \n",
       "enttype         0.212679   0.184072  0.184857   0.999995  \n",
       "enttypeitem     0.454545   0.317294  0.321941   1.000000  \n",
       "state          -0.046624  -0.041558 -0.043007  -0.027873  \n",
       "orgid           0.003824   0.003426  0.003553   0.179710  \n",
       "jobid          -0.019219   0.000662  0.013314  -0.178719  \n",
       "adbusign       -0.032364  -0.028940 -0.030020  -0.042538  \n",
       "townsign       -0.077001  -0.068255 -0.070828   0.346336  \n",
       "regtype         0.083947   0.142100  0.142878  -0.097343  \n",
       "empnum          0.116721   0.165063  0.134318  -0.034006  \n",
       "compform             NaN        NaN       NaN   0.023961  \n",
       "parnum               NaN   1.000000       NaN   0.260902  \n",
       "exenum               NaN        NaN       NaN  -0.000703  \n",
       "ptbusscope           NaN        NaN       NaN        NaN  \n",
       "venind               NaN        NaN       NaN   0.275019  \n",
       "enttypeminu          NaN        NaN       NaN   1.000000  \n",
       "midpreindcode        NaN        NaN       NaN        NaN  \n",
       "protype         0.173116   0.156798  0.147581  -0.082259  \n",
       "regcap         -0.018039  -0.017124 -0.012161  -0.008523  \n",
       "reccap         -0.022261  -0.027902 -0.025398   0.095021  \n",
       "forreccap       1.000000   0.650067  0.704133   0.214113  \n",
       "forregcap       0.650067   1.000000  0.964826   0.185152  \n",
       "congro          0.704133   0.964826  1.000000   0.185857  \n",
       "enttypegb       0.214113   0.185152  0.185857   1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_info.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集2：annual_report_info.csv\n",
    "\n",
    "包含数据集7和8中涉及到的企业的年报基本信息，每一行代表一个企业的年报基本数据，每一行有23列，其中id列为企业唯一标识，列之间采用“,”分隔符分割。\n",
    "\n",
    "数据格式如下：\n",
    "\n",
    "|   列名   |  含义    |   列名   |   含义   | \n",
    "| ---- | ---- | ---- | ---- |\n",
    "| id | 企业唯一标识 | ANCHEYEAR | 年度 |\n",
    "| STATE | 状态 | FUNDAM | 资金数额 |\n",
    "| MEMNUM | 成员人数 | FARNUM | 农民人数 |\n",
    "| ANNNEWMEMNUM | 本年度新增成员人数 | ANNREDMEMNUM | 本年度退出成员人数 |\n",
    "| EMPNUM | 从业人数 | EMPNUMSIGN | 从业人数是否公示 |\n",
    "| BUSSTNAME | 经营状态名称 | COLGRANUM | 其中高校毕业生人数经营者 |\n",
    "| RETSOLNUM | 其中退役士兵人数经营者 | DISPERNUM | 其中残疾人人数经营者 |\n",
    "| UNENUM | 其中下岗失业人数经营者 | COLEMPLNUM | 其中高校毕业生人数雇员 |\n",
    "| RETEMPLNUM | 其中退役士兵人数雇员 | DISEMPLNUM | 其中残疾人人数雇员 |\n",
    "| UNEEMPLNUM | 其中下岗失业人数雇员 | WEBSITSIGN | 是否有网站标志 |\n",
    "| FORINVESTSIGN | 是否有对外投资企业标志 | STOCKTRANSIGN | 有限责任公司本年度是否发生股东股权转让标志 |\n",
    "| PUBSTATE | 公示状态：1 全部公示，2部分公示，3全部不公示|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T03:06:16.504079Z",
     "start_time": "2020-11-19T03:06:16.437889Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22550, 23) 8937\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>ANCHEYEAR</th>\n",
       "      <th>STATE</th>\n",
       "      <th>FUNDAM</th>\n",
       "      <th>MEMNUM</th>\n",
       "      <th>FARNUM</th>\n",
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       "      <th>ANNREDMEMNUM</th>\n",
       "      <th>EMPNUM</th>\n",
       "      <th>EMPNUMSIGN</th>\n",
       "      <th>BUSSTNAME</th>\n",
       "      <th>COLGRANUM</th>\n",
       "      <th>RETSOLNUM</th>\n",
       "      <th>DISPERNUM</th>\n",
       "      <th>UNENUM</th>\n",
       "      <th>COLEMPLNUM</th>\n",
       "      <th>RETEMPLNUM</th>\n",
       "      <th>DISEMPLNUM</th>\n",
       "      <th>UNEEMPLNUM</th>\n",
       "      <th>WEBSITSIGN</th>\n",
       "      <th>FORINVESTSIGN</th>\n",
       "      <th>STOCKTRANSIGN</th>\n",
       "      <th>PUBSTATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f000950527a6feb63ee1ce82bb22ddd1ab8b8fdffa3b91fb</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>开业</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f000950527a6feb63ee1ce82bb22ddd1ab8b8fdffa3b91fb</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>开业</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9c7fa510616a68309e4badf2a7a3123c0462fb85bf28ef17</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 id  ANCHEYEAR  STATE  FUNDAM  \\\n",
       "0  9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3     2017.0    2.0     5.0   \n",
       "1  9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3     2018.0    2.0     2.0   \n",
       "2  f000950527a6feb63ee1ce82bb22ddd1ab8b8fdffa3b91fb     2017.0    2.0     NaN   \n",
       "3  f000950527a6feb63ee1ce82bb22ddd1ab8b8fdffa3b91fb     2018.0    2.0     NaN   \n",
       "4  9c7fa510616a68309e4badf2a7a3123c0462fb85bf28ef17     2017.0    2.0     5.0   \n",
       "\n",
       "   MEMNUM  FARNUM  ANNNEWMEMNUM  ANNREDMEMNUM  EMPNUM  EMPNUMSIGN BUSSTNAME  \\\n",
       "0     NaN     NaN           NaN           NaN    10.0         NaN       NaN   \n",
       "1     NaN     NaN           NaN           NaN     2.0         NaN       NaN   \n",
       "2     NaN     NaN           NaN           NaN     4.0         2.0        开业   \n",
       "3     NaN     NaN           NaN           NaN     3.0         2.0        开业   \n",
       "4     NaN     NaN           NaN           NaN    10.0         NaN       NaN   \n",
       "\n",
       "   COLGRANUM  RETSOLNUM  DISPERNUM  UNENUM  COLEMPLNUM  RETEMPLNUM  \\\n",
       "0        0.0        0.0        0.0     0.0         0.0         0.0   \n",
       "1        0.0        0.0        0.0     0.0         0.0         0.0   \n",
       "2        3.0        0.0        0.0     0.0         1.0         0.0   \n",
       "3        1.0        0.0        0.0     0.0         2.0         0.0   \n",
       "4        0.0        0.0        0.0     0.0         0.0         0.0   \n",
       "\n",
       "   DISEMPLNUM  UNEEMPLNUM  WEBSITSIGN  FORINVESTSIGN  STOCKTRANSIGN  PUBSTATE  \n",
       "0         0.0         0.0         2.0            NaN            NaN       3.0  \n",
       "1         0.0         0.0         2.0            NaN            NaN       3.0  \n",
       "2         0.0         0.0         2.0            2.0            2.0       3.0  \n",
       "3         0.0         0.0         2.0            2.0            2.0       3.0  \n",
       "4         0.0         0.0         2.0            NaN            NaN       3.0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_report_info = pd.read_csv(PATH + 'annual_report_info.csv')\n",
    "print(annual_report_info.shape, annual_report_info['id'].nunique())\n",
    "\n",
    "annual_report_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T17:16:42.851240Z",
     "start_time": "2020-11-18T17:16:42.358558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 缺失值\n",
    "msno.bar(annual_report_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集3：tax_info.csv\n",
    "\n",
    "包含数据集7和8中涉及到的企业的纳税信息，每一行代表一个企业的纳税信息，每一行有9列，其中id列为企业唯一标识，列之间采用“,”分隔符分割。\n",
    "\n",
    "数据格式如下：\n",
    "\n",
    "|   列名   |  含义    |   列名   |   含义   | \n",
    "| ---- | ---- | ---- | ---- |\n",
    "| id | 企业唯一标识 | START_DATE | 起始时间 |\n",
    "| END_DATE | 终止时间 |  TAX_CATEGORIES | 税种 |\n",
    "| TAX_ITEMS | 税目 | TAXATION_BASIS | 计税依据 |\n",
    "| TAX_RATE | 税率 | DEDUCTION | 扣除数 |\n",
    "| TAX_AMOUNT | 税额 | | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T03:06:41.066155Z",
     "start_time": "2020-11-19T03:06:41.014573Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29195, 9) 808\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>START_DATE</th>\n",
       "      <th>END_DATE</th>\n",
       "      <th>TAX_CATEGORIES</th>\n",
       "      <th>TAX_ITEMS</th>\n",
       "      <th>TAXATION_BASIS</th>\n",
       "      <th>TAX_RATE</th>\n",
       "      <th>DEDUCTION</th>\n",
       "      <th>TAX_AMOUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f000950527a6feb6c2f40c9d8477e73a439dfa0897830397</td>\n",
       "      <td>2015/09/01</td>\n",
       "      <td>2015/09/30</td>\n",
       "      <td>印花税</td>\n",
       "      <td>工伤保险（单位）</td>\n",
       "      <td>72530.75</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f000950527a6feb6c2f40c9d8477e73a439dfa0897830397</td>\n",
       "      <td>2015/09/01</td>\n",
       "      <td>2015/09/30</td>\n",
       "      <td>印花税</td>\n",
       "      <td>失业保险（单位）</td>\n",
       "      <td>72530.75</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f000950527a6feb6c2f40c9d8477e73a439dfa0897830397</td>\n",
       "      <td>2015/09/01</td>\n",
       "      <td>2015/09/30</td>\n",
       "      <td>印花税</td>\n",
       "      <td>医疗保险（单位）</td>\n",
       "      <td>72530.75</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>f000950527a6feb6c2f40c9d8477e73a439dfa0897830397</td>\n",
       "      <td>2015/09/01</td>\n",
       "      <td>2015/09/30</td>\n",
       "      <td>印花税</td>\n",
       "      <td>企业养老保险基金（单位）</td>\n",
       "      <td>72530.75</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f000950527a6feb6c2f40c9d8477e73a439dfa0897830397</td>\n",
       "      <td>2015/09/01</td>\n",
       "      <td>2015/09/30</td>\n",
       "      <td>印花税</td>\n",
       "      <td>烟叶收购</td>\n",
       "      <td>72530.75</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 id  START_DATE    END_DATE  \\\n",
       "0  f000950527a6feb6c2f40c9d8477e73a439dfa0897830397  2015/09/01  2015/09/30   \n",
       "1  f000950527a6feb6c2f40c9d8477e73a439dfa0897830397  2015/09/01  2015/09/30   \n",
       "2  f000950527a6feb6c2f40c9d8477e73a439dfa0897830397  2015/09/01  2015/09/30   \n",
       "3  f000950527a6feb6c2f40c9d8477e73a439dfa0897830397  2015/09/01  2015/09/30   \n",
       "4  f000950527a6feb6c2f40c9d8477e73a439dfa0897830397  2015/09/01  2015/09/30   \n",
       "\n",
       "  TAX_CATEGORIES     TAX_ITEMS  TAXATION_BASIS  TAX_RATE  DEDUCTION  \\\n",
       "0            印花税      工伤保险（单位）        72530.75    0.0003      -0.04   \n",
       "1            印花税      失业保险（单位）        72530.75    0.0003      -0.04   \n",
       "2            印花税      医疗保险（单位）        72530.75    0.0003      -0.04   \n",
       "3            印花税  企业养老保险基金（单位）        72530.75    0.0003      -0.04   \n",
       "4            印花税          烟叶收购        72530.75    0.0003      -0.04   \n",
       "\n",
       "   TAX_AMOUNT  \n",
       "0        21.8  \n",
       "1        21.8  \n",
       "2        21.8  \n",
       "3        21.8  \n",
       "4        21.8  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_info = pd.read_csv(PATH + 'tax_info.csv')\n",
    "print(tax_info.shape, tax_info['id'].nunique())\n",
    "\n",
    "tax_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T17:14:59.469514Z",
     "start_time": "2020-11-18T17:14:59.143614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 缺失值\n",
    "msno.bar(tax_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集4：change_info.csv\n",
    "\n",
    "包含数据集7和8中涉及到的企业的变更信息，每一行代表一个企业变更信息，每一行5列，其中id列为企业唯一标识，列之间采用“,”分隔符分割。\n",
    "\n",
    "数据格式如下：\n",
    "\n",
    "|   列名   |  含义    |   列名   |   含义   | \n",
    "| ---- | ---- | ---- | ---- |\n",
    "| id | 企业唯一标识 | bgxmdm | 变更信息代码 |\n",
    "| bgq | 变更前 | bgh | 变更后 |\n",
    "| bgrq | 变更日期 | |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T16:48:43.324675Z",
     "start_time": "2020-11-18T16:48:43.194657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>bgxmdm</th>\n",
       "      <th>bgq</th>\n",
       "      <th>bgh</th>\n",
       "      <th>bgrq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3</td>\n",
       "      <td>939.0</td>\n",
       "      <td>9dec12da51cdb672a91b4a8ae0e0895f7bfeb243dfa3e0c8</td>\n",
       "      <td>9dec12da51cdb672a91b4a8ae0e0895f4a56cbe3deca98...</td>\n",
       "      <td>2.019060e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3</td>\n",
       "      <td>112.0</td>\n",
       "      <td>31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...</td>\n",
       "      <td>31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...</td>\n",
       "      <td>2.019060e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e9f7b28ec10e047000d16ab79e1b5e6da434a1697cce7818</td>\n",
       "      <td>111.0</td>\n",
       "      <td>54ca436ffb87f24c820178b45fcc3a7b</td>\n",
       "      <td>f80e3376abcf81ad2a279d6d99046153</td>\n",
       "      <td>2.017013e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e9f7b28ec10e047000d16ab79e1b5e6da434a1697cce7818</td>\n",
       "      <td>128.0</td>\n",
       "      <td>f1fdb1c866dc96638cbfb8b788b91393</td>\n",
       "      <td>1eca8a0d8beca58d988f7dccab5dc868</td>\n",
       "      <td>2.017013e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e9f7b28ec10e047000d16ab79e1b5e6da434a1697cce7818</td>\n",
       "      <td>925.0</td>\n",
       "      <td>54ca436ffb87f24c820178b45fcc3a7b</td>\n",
       "      <td>f80e3376abcf81ad2a279d6d99046153</td>\n",
       "      <td>2.017013e+13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 id  bgxmdm  \\\n",
       "0  9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3   939.0   \n",
       "1  9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3   112.0   \n",
       "2  e9f7b28ec10e047000d16ab79e1b5e6da434a1697cce7818   111.0   \n",
       "3  e9f7b28ec10e047000d16ab79e1b5e6da434a1697cce7818   128.0   \n",
       "4  e9f7b28ec10e047000d16ab79e1b5e6da434a1697cce7818   925.0   \n",
       "\n",
       "                                                 bgq  \\\n",
       "0   9dec12da51cdb672a91b4a8ae0e0895f7bfeb243dfa3e0c8   \n",
       "1  31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...   \n",
       "2                   54ca436ffb87f24c820178b45fcc3a7b   \n",
       "3                   f1fdb1c866dc96638cbfb8b788b91393   \n",
       "4                   54ca436ffb87f24c820178b45fcc3a7b   \n",
       "\n",
       "                                                 bgh          bgrq  \n",
       "0  9dec12da51cdb672a91b4a8ae0e0895f4a56cbe3deca98...  2.019060e+13  \n",
       "1  31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...  2.019060e+13  \n",
       "2                   f80e3376abcf81ad2a279d6d99046153  2.017013e+13  \n",
       "3                   1eca8a0d8beca58d988f7dccab5dc868  2.017013e+13  \n",
       "4                   f80e3376abcf81ad2a279d6d99046153  2.017013e+13  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_info = pd.read_csv(PATH + 'change_info.csv')\n",
    "change_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T17:15:18.681770Z",
     "start_time": "2020-11-18T17:15:18.423875Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 缺失值\n",
    "msno.bar(change_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集5：news_info.csv\n",
    "\n",
    "包含数据集7和8中涉及到的企业的新闻舆情信息，每一行代表一个企业新闻舆情，每一行3列，其中id列为企业唯一标识，列之间采用“,”分隔符分割。\n",
    "\n",
    "数据格式如下：\n",
    "\n",
    "|   列名   |  含义    |   列名   |   含义   | \n",
    "| ---- | ---- | ---- | ---- |\n",
    "| id | 企业唯一标识 | positive_negtive | 新闻正负面性 |\n",
    "| public_date | 发布日期 | |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T16:49:20.014528Z",
     "start_time": "2020-11-18T16:49:19.994684Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>positive_negtive</th>\n",
       "      <th>public_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f000950527a6feb62669d6a175fe6fdccd1eb4f7ca8e5016</td>\n",
       "      <td>积极</td>\n",
       "      <td>2016-12-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f000950527a6feb6e8bd9919e2ca363359bcfa997a0f9de7</td>\n",
       "      <td>中立</td>\n",
       "      <td>2017-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f000950527a6feb6e8bd9919e2ca363359bcfa997a0f9de7</td>\n",
       "      <td>消极</td>\n",
       "      <td>2016-02-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d8071a739aa75a3bcf6fb0041ee883243251d30025ab9d45</td>\n",
       "      <td>中立</td>\n",
       "      <td>2018-06-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f000950527a6feb6d71de3382afa0bc5ff87bb65477f698a</td>\n",
       "      <td>积极</td>\n",
       "      <td>2015-06-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 id positive_negtive  \\\n",
       "0  f000950527a6feb62669d6a175fe6fdccd1eb4f7ca8e5016               积极   \n",
       "1  f000950527a6feb6e8bd9919e2ca363359bcfa997a0f9de7               中立   \n",
       "2  f000950527a6feb6e8bd9919e2ca363359bcfa997a0f9de7               消极   \n",
       "3  d8071a739aa75a3bcf6fb0041ee883243251d30025ab9d45               中立   \n",
       "4  f000950527a6feb6d71de3382afa0bc5ff87bb65477f698a               积极   \n",
       "\n",
       "  public_date  \n",
       "0  2016-12-30  \n",
       "1  2017-08-09  \n",
       "2  2016-02-29  \n",
       "3  2018-06-08  \n",
       "4  2015-06-29  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_info = pd.read_csv(PATH + 'news_info.csv')\n",
    "news_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集6：other_info.csv\n",
    "\n",
    "包含数据集7和8中涉及到的企业的其他信息，每一行代表一个企业其他信息，每一行4列，其中id列为企业唯一标识，列之间采用“,”分隔符分割。\n",
    "\n",
    "数据格式如下：\n",
    "\n",
    "|   列名   |  含义    |   列名   |   含义   | \n",
    "| ---- | ---- | ---- | ---- |\n",
    "| id | 企业唯一标识 | legal_judgment_num | 裁判文书数量 |\n",
    "| brand_num | 注册商标数量 | patent_num | 专利数量|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T16:49:44.508510Z",
     "start_time": "2020-11-18T16:49:44.495793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>legal_judgment_num</th>\n",
       "      <th>brand_num</th>\n",
       "      <th>patent_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f000950527a6feb6d340f91da09e61347d8200cd2f0d1602</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>f000950527a6feb608dd9322b74a99f60851207f36a3c94c</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d8071a739aa75a3b9f23966f8dae78fd226c272515b9c255</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>216bd2aaf4d079242209b1496f81a36c7abed9dd0bb65ed3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e9f7b28ec10e0470de9631c789f49acdd4e7cf9ed6db094b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 id  legal_judgment_num  \\\n",
       "0  f000950527a6feb6d340f91da09e61347d8200cd2f0d1602                 4.0   \n",
       "1  f000950527a6feb608dd9322b74a99f60851207f36a3c94c                 1.0   \n",
       "2  d8071a739aa75a3b9f23966f8dae78fd226c272515b9c255                 2.0   \n",
       "3  216bd2aaf4d079242209b1496f81a36c7abed9dd0bb65ed3                 NaN   \n",
       "4  e9f7b28ec10e0470de9631c789f49acdd4e7cf9ed6db094b                 NaN   \n",
       "\n",
       "   brand_num  patent_num  \n",
       "0        NaN         NaN  \n",
       "1        NaN         NaN  \n",
       "2        NaN         NaN  \n",
       "3        1.0         NaN  \n",
       "4        2.0         NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_info = pd.read_csv(PATH + 'other_info.csv')\n",
    "other_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T17:15:42.588583Z",
     "start_time": "2020-11-18T17:15:42.347642Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 缺失值\n",
    "msno.bar(other_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集7：entprise_info.csv\n",
    "\n",
    "带标注的企业数据。每一行代表一个企业，每一行2列，其中id列为企业唯一标识，label列为标注（1：有非法集资风险，0：无非法集资风险），列之间采用“,”分隔符分割。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:49:29.960356Z",
     "start_time": "2020-11-21T02:49:29.858279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14865, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entprise_info = pd.read_csv(PATH + 'entprise_info.csv')\n",
    "entprise_info.head()\n",
    "entprise_info.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集8（验证集）：entprise_evaluate.csv\n",
    "\n",
    "未标注企业数据。参赛队伍需提交的最终结果数据集，每一行代表一个企业，每一行有 2 列, 其中id列为企业唯一标识，score列为空，列之间采用“,”分隔符分割。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:49:30.641666Z",
     "start_time": "2020-11-21T02:49:30.581818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entprise_evaluate = pd.read_csv('../input/' + 'entprise_evaluate.csv')\n",
    "entprise_evaluate.head()\n",
    "entprise_evaluate.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提交要求\n",
    "\n",
    "参赛者以csv文件提交：参赛者以csv文件格式，提交模型结果到大数据竞赛平台，平台进行在线评分，实时排名。目前平台仅支持单文件提交，即所有提交内容需要放在一个文件中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评测标准\n",
    "\n",
    "本赛题采用分类任务的精确率 P（precision）、召回率 R（recall） 和 F1 -score三个指标作为模型性能的评判标准。\n",
    "\n",
    "| 真实结果 |         预测结果         |           描述           |\n",
    "| :------: | :----------------------: | :----------------------: |\n",
    "| 真实结果 |           正例           |           负例           |\n",
    "|   正例   | TP(True Positive真正例)  | FN(False Negative假负例) |\n",
    "|   负例   | FP(False Positive假正例) | TN(True Negative真负例)  |\n",
    "\n",
    "\n",
    "在计算精确率、召回率和 F1-score时，系统会自动将提交结果中概率大于0.5的识别为1（有非法集资风险），概率小于等于0.5的识别为0（无非法集资风险）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:48:32.620473Z",
     "start_time": "2020-11-21T02:48:32.616366Z"
    }
   },
   "outputs": [],
   "source": [
    "def filter_col_by_nan(df, ratio=0.05):\n",
    "    cols = []\n",
    "    for col in df.columns:\n",
    "        if df[col].isna().mean() >= (1-ratio):\n",
    "            cols.append(col)\n",
    "    return cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:59:03.514368Z",
     "start_time": "2020-11-21T02:59:02.966580Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 剔除纯空列\n",
    "base_info = pd.read_csv(PATH + 'base_info.csv')\n",
    "base_info = base_info.drop(filter_col_by_nan(base_info, 0.01), axis=1)\n",
    "\n",
    "annual_report_info = pd.read_csv(PATH + 'annual_report_info.csv')\n",
    "annual_report_info = annual_report_info.drop(filter_col_by_nan(annual_report_info, 0.01), axis=1)\n",
    "\n",
    "other_info = pd.read_csv(PATH + 'other_info.csv')\n",
    "other_info = other_info[~other_info['id'].duplicated()]\n",
    "other_info['other_SUM'] = other_info[['legal_judgment_num', 'brand_num', 'patent_num']].sum(1)\n",
    "other_info['other_NULL_SUM'] = other_info[['legal_judgment_num', 'brand_num', 'patent_num']].isnull().astype(int).sum(1)\n",
    "\n",
    "\n",
    "news_info = pd.read_csv(PATH + 'news_info.csv')\n",
    "news_info['public_date'] = news_info['public_date'].apply(lambda x: x if '-' in str(x) else np.nan)\n",
    "news_info['public_date'] = pd.to_datetime(news_info['public_date'])\n",
    "news_info['public_date'] = (datetime.now() - news_info['public_date']).dt.days\n",
    "\n",
    "news_info_df = news_info.groupby('id').agg({'public_date': ['count','max','min','mean']}).reset_index()\n",
    "news_info_df.columns = ['id', 'public_date_COUNT', 'public_MAX', 'public_MIN', 'public_MEAN']\n",
    "news_info_df2 = pd.pivot_table(news_info, index='id', columns='positive_negtive', aggfunc='count').reset_index()\n",
    "news_info_df2.columns = ['id', 'news_COUNT1', 'news_COUNT2', 'news_COUNT3']\n",
    "news_info_df = pd.merge(news_info_df, news_info_df2)\n",
    "\n",
    "tax_info = pd.read_csv(PATH + 'tax_info.csv')\n",
    "tax_info_df = tax_info.groupby('id').agg({\n",
    "    'TAX_CATEGORIES': ['count'],\n",
    "    'TAX_ITEMS': ['count'],\n",
    "    'TAXATION_BASIS': ['count'],\n",
    "    'TAX_AMOUNT': ['max', 'min', 'mean'],\n",
    "})\n",
    "tax_info_df.columns = pd.Index(['PREV_' + e[0] + \"_\" + e[1].upper() \n",
    "                                for e in tax_info_df.columns.tolist()])\n",
    "tax_info_df = tax_info_df.reset_index()\n",
    "\n",
    "change_info = pd.read_csv(PATH + 'change_info.csv')\n",
    "change_info['bgrq'] = (change_info['bgrq'] / 10000000000).astype(int)\n",
    "\n",
    "change_info_df = change_info.groupby('id').agg({\n",
    "    'bgxmdm': ['count', 'nunique'],\n",
    "    'bgq':['nunique'],\n",
    "    'bgh': ['nunique'],\n",
    "    'bgrq': ['nunique'],\n",
    "})\n",
    "change_info_df.columns = pd.Index(['changeinfo_' + e[0] + \"_\" + e[1].upper() \n",
    "                                for e in change_info_df.columns.tolist()])\n",
    "change_info_df = change_info_df.reset_index()\n",
    "\n",
    "annual_report_info = pd.read_csv(PATH + 'annual_report_info.csv')\n",
    "annual_report_info_df = annual_report_info.groupby('id').agg({\n",
    "    'ANCHEYEAR': ['max'],\n",
    "    'STATE': ['max'],\n",
    "    'FUNDAM': ['max'],\n",
    "    'EMPNUM': ['max'],\n",
    "    'UNEEMPLNUM': ['max', 'sum']\n",
    "})\n",
    "annual_report_info_df.columns = pd.Index(['PREV_' + e[0] + \"_\" + e[1].upper() \n",
    "                                for e in annual_report_info_df.columns.tolist()])\n",
    "annual_report_info_df = annual_report_info_df.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:59:35.032774Z",
     "start_time": "2020-11-21T02:59:03.602007Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "base_info['district_FLAG1'] = (base_info['orgid'].fillna('').apply(lambda x: str(x)[:6]) == \\\n",
    "    base_info['oplocdistrict'].fillna('').apply(lambda x: str(x)[:6])).astype(int)\n",
    "base_info['district_FLAG2'] = (base_info['orgid'].fillna('').apply(lambda x: str(x)[:6]) == \\\n",
    "    base_info['jobid'].fillna('').apply(lambda x: str(x)[:6])).astype(int)\n",
    "base_info['district_FLAG3'] = (base_info['oplocdistrict'].fillna('').apply(lambda x: str(x)[:6]) == \\\n",
    "    base_info['jobid'].fillna('').apply(lambda x: str(x)[:6])).astype(int)\n",
    "\n",
    "base_info['person_SUM'] = base_info[['empnum', 'parnum', 'exenum']].sum(1)\n",
    "base_info['person_NULL_SUM'] = base_info[['empnum', 'parnum', 'exenum']].isnull().astype(int).sum(1)\n",
    "\n",
    "# base_info['regcap_DIVDE_empnum'] = base_info['regcap'] / base_info['empnum']\n",
    "# base_info['regcap_DIVDE_exenum'] = base_info['regcap'] / base_info['exenum']\n",
    "\n",
    "# base_info['reccap_DIVDE_empnum'] = base_info['reccap'] / base_info['empnum']\n",
    "# base_info['regcap_DIVDE_exenum'] = base_info['regcap'] / base_info['exenum']\n",
    "\n",
    "# base_info['congro_DIVDE_empnum'] = base_info['congro'] / base_info['empnum']\n",
    "# base_info['regcap_DIVDE_exenum'] = base_info['regcap'] / base_info['exenum']\n",
    "\n",
    "base_info['opfrom'] = pd.to_datetime(base_info['opfrom'])\n",
    "base_info['opto'] = pd.to_datetime(base_info['opto'])\n",
    "base_info['opfrom_TONOW'] = (datetime.now() - base_info['opfrom']).dt.days\n",
    "base_info['opfrom_TIME'] = (base_info['opto'] - base_info['opfrom']).dt.days\n",
    "\n",
    "base_info['opscope_COUNT'] = base_info['opscope'].apply(lambda x: len(x.replace(\"\\t\", \"，\").replace(\"\\n\", \"，\").split('、')))\n",
    "\n",
    "cat_col = ['oplocdistrict', 'industryphy', 'industryco', 'enttype',\n",
    "           'enttypeitem', 'enttypeminu', 'enttypegb',\n",
    "          'dom', 'oploc', 'opform']\n",
    "\n",
    "for col in cat_col:\n",
    "    base_info[col + '_COUNT'] = base_info[col].map(base_info[col].value_counts())\n",
    "    col_idx = base_info[col].value_counts()\n",
    "    for idx in col_idx[col_idx < 10].index:\n",
    "        base_info[col] = base_info[col].replace(idx, -1)        \n",
    "\n",
    "# base_info['opscope'] = base_info['opscope'].apply(lambda x: x.replace(\"\\t\", \" \").replace(\"\\n\", \" \").replace(\"，\", \" \"))\n",
    "# clf_tfidf = TfidfVectorizer(max_features=200)\n",
    "# tfidf=clf_tfidf.fit_transform(base_info['opscope'])\n",
    "# tfidf = pd.DataFrame(tfidf.toarray())\n",
    "# tfidf.columns = ['opscope_' + str(x) for x in range(200)]\n",
    "# base_info = pd.concat([base_info, tfidf], axis=1)\n",
    "\n",
    "base_info = base_info.drop(['opfrom', 'opto'], axis=1)\n",
    "\n",
    "for col in ['industryphy', 'dom', 'opform', 'oploc']:\n",
    "    base_info[col] = pd.factorize(base_info[col])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:59:35.187809Z",
     "start_time": "2020-11-21T02:59:35.034347Z"
    }
   },
   "outputs": [],
   "source": [
    "train_data = pd.merge(base_info, entprise_info, on='id')\n",
    "train_data = pd.merge(train_data, other_info, on='id', how='left')\n",
    "\n",
    "train_data = pd.merge(train_data, news_info_df, on='id', how='left')\n",
    "train_data = pd.merge(train_data, tax_info_df, on='id', how='left')\n",
    "train_data = pd.merge(train_data, annual_report_info_df, on='id', how='left')\n",
    "train_data = pd.merge(train_data, change_info_df, on='id', how='left')\n",
    "\n",
    "\n",
    "entprise_evaluate = pd.read_csv('../input/' + 'entprise_evaluate.csv')\n",
    "entprise_evaluate = entprise_evaluate[['id']]\n",
    "test_data = pd.merge(base_info, entprise_evaluate, on='id')\n",
    "test_data = pd.merge(test_data, other_info, on='id', how='left')\n",
    "test_data = pd.merge(test_data, news_info_df, on='id', how='left')\n",
    "test_data = pd.merge(test_data, tax_info_df, on='id', how='left')\n",
    "test_data = pd.merge(test_data, annual_report_info_df, on='id', how='left')\n",
    "test_data = pd.merge(test_data, change_info_df, on='id', how='left')\n",
    "\n",
    "\n",
    "# train_data['enttypeminu_RANK'] = train_data['enttypeminu'].map(train_data.groupby(['enttypeminu'])['label'].mean().rank())\n",
    "# train_data['enttypeitem_RANK'] = train_data['enttypeitem'].map(train_data.groupby(['enttypeitem'])['label'].mean().rank())\n",
    "# test_data['enttypeminu_RANK'] = test_data['enttypeminu'].map(train_data.groupby(['enttypeminu'])['label'].mean().rank())\n",
    "# test_data['enttypeitem_RANK'] = test_data['enttypeitem'].map(train_data.groupby(['enttypeitem'])['label'].mean().rank())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T03:00:03.697067Z",
     "start_time": "2020-11-21T03:00:03.687072Z"
    }
   },
   "outputs": [],
   "source": [
    "def eval_score(y_test,y_pre):\n",
    "    _,_,f_class,_=precision_recall_fscore_support(y_true=y_test,y_pred=y_pre,labels=[0,1],average=None)\n",
    "    fper_class={'合法':f_class[0],'违法':f_class[1],'f1':f1_score(y_test,y_pre)}\n",
    "    return fper_class\n",
    "\n",
    "\n",
    "def k_fold_serachParmaters(model,train_val_data,train_val_kind, test_kind):\n",
    "    mean_f1=0\n",
    "    mean_f1Train=0\n",
    "    n_splits=5\n",
    "    \n",
    "    cat_features = ['oplocdistrict', 'industryphy', 'industryco', 'enttype',\n",
    "           'enttypeitem', 'enttypeminu', 'enttypegb',\n",
    "          'dom', 'oploc', 'opform']\n",
    "    \n",
    "    sk = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=2020)\n",
    "    pred_Test = np.zeros(len(test_kind))\n",
    "    for train, test in sk.split(train_val_data, train_val_kind):\n",
    "        x_train = train_val_data.iloc[train]\n",
    "        y_train = train_val_kind.iloc[train]\n",
    "        x_test = train_val_data.iloc[test]\n",
    "        y_test = train_val_kind.iloc[test]\n",
    "\n",
    "        model.fit(x_train, y_train, \n",
    "                  eval_set=[(x_test, y_test)], \n",
    "                  categorical_feature = cat_features,\n",
    "                 early_stopping_rounds=100,\n",
    "                 verbose=False)\n",
    "        \n",
    "        pred = model.predict(x_test)\n",
    "        fper_class = eval_score(y_test,pred)\n",
    "        \n",
    "        pred_Train = model.predict(x_train)\n",
    "        pred_Test += model.predict_proba(test_kind)[:, 1]/n_splits\n",
    "        fper_class_train = eval_score(y_train,pred_Train)\n",
    "\n",
    "        mean_f1 += fper_class['f1']/n_splits\n",
    "        mean_f1Train+=fper_class_train['f1']/n_splits\n",
    "        # print(mean_f1, mean_f1Train)\n",
    "        \n",
    "        \n",
    "    return mean_f1, pred_Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T03:00:24.841488Z",
     "start_time": "2020-11-21T03:00:04.258257Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.842809925440738 0.0017004331407223137\n"
     ]
    }
   ],
   "source": [
    "score_tta = None\n",
    "score_list = []\n",
    "\n",
    "tta_fold = 20\n",
    "for _ in range(tta_fold):\n",
    "    clf = lgb.LGBMClassifier(\n",
    "        num_leaves=np.random.randint(6, 10), min_child_samples= np.random.randint(2,5),\n",
    "        max_depth=5,learning_rate=0.03,\n",
    "        n_estimators=150,n_jobs=-1)\n",
    "\n",
    "    score, test_pred = k_fold_serachParmaters(clf,\n",
    "                           train_data.drop(['id', 'opscope','label'], axis=1),\n",
    "                           train_data['label'],\n",
    "                           test_data.drop(['id', 'opscope'], axis=1),\n",
    "                          )\n",
    "\n",
    "    if score_tta is None:\n",
    "        score_tta = test_pred/tta_fold\n",
    "    else:\n",
    "        score_tta += test_pred/tta_fold\n",
    "    # print(score)\n",
    "    score_list.append(score)\n",
    "    \n",
    "print(np.array(score_list).mean(), np.array(score_list).std())\n",
    "# 0.8478168974849689 0.83884757\n",
    "\n",
    "# 0.8420447002972562 0.00198977186270193\n",
    "# 0.8430490420761639 0.0022246925904664443"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:57:21.530293Z",
     "start_time": "2020-11-21T02:57:21.524992Z"
    }
   },
   "outputs": [],
   "source": [
    "test_data['score'] = score_tta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:57:22.200318Z",
     "start_time": "2020-11-21T02:57:22.166588Z"
    }
   },
   "outputs": [],
   "source": [
    "test_data[['id', 'score']].to_csv('tmp.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T18:39:35.946566Z",
     "start_time": "2020-11-19T18:39:21.217407Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/lightgbm/basic.py:1209: UserWarning: categorical_feature in Dataset is overridden.\n",
      "New categorical_feature is ['dom', 'enttype', 'enttypegb', 'enttypeitem', 'enttypeminu', 'industryco', 'industryphy', 'opform', 'oploc', 'oplocdistrict']\n",
      "  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(LGBMClassifier(learning_rate=0.05, max_depth=7, n_estimators=150, num_leaves=7,\n",
       "                silent=False),\n",
       " 0.8461400509259527)"
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg = lgb.LGBMClassifier(silent=False)\n",
    "param_dist = {\"max_depth\": [4,5,7],\n",
    "              \"learning_rate\" : [0.01,0.05],\n",
    "              \"num_leaves\": [5, 7, 8],\n",
    "              \"n_estimators\": [50, 100, 150]\n",
    "             }\n",
    "\n",
    "cat_features = ['oplocdistrict', 'industryphy', 'industryco', 'enttype',\n",
    "           'enttypeitem', 'enttypeminu', 'enttypegb',\n",
    "          'dom', 'oploc', 'opform']\n",
    "\n",
    "grid_search = GridSearchCV(lg, n_jobs=-1, param_grid=param_dist, cv = 5, scoring='f1', verbose=5)\n",
    "grid_search.fit(train_data.drop(['id', 'opscope','label'], axis=1),\n",
    "                           train_data['label'], categorical_feature = cat_features,)\n",
    "grid_search.best_estimator_, grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T11:02:32.006619Z",
     "start_time": "2020-11-19T11:02:31.979882Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T11:07:02.832994Z",
     "start_time": "2020-11-19T11:07:02.540322Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T18:18:15.709144Z",
     "start_time": "2020-11-19T18:18:15.695243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>empnum</th>\n",
       "      <th>parnum</th>\n",
       "      <th>exenum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>24862</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24863</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24864</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "       empnum  parnum  exenum\n",
       "0         5.0     NaN     NaN\n",
       "1         3.0     NaN     NaN\n",
       "2         2.0     1.0     NaN\n",
       "3         2.0     NaN     NaN\n",
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       "24863     3.0     NaN     NaN\n",
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       "\n",
       "[24865 rows x 3 columns]"
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     },
     "execution_count": 647,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "base_info[['empnum', 'parnum', 'exenum']]"
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   "execution_count": 648,
   "metadata": {
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  {
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   "execution_count": 669,
   "metadata": {
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   "execution_count": 711,
   "metadata": {
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     "start_time": "2020-11-19T18:43:05.285141Z"
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       "person_NULL_SUM            0.360628\n",
       "regcap_DIVDE_empnum        0.204050\n",
       "opfrom_TONOW               0.381781\n",
       "opfrom_TIME                0.498443\n",
       "industryphy_COUNT          0.400440\n",
       "enttype_COUNT              0.332443\n",
       "enttypeitem_COUNT          0.437443\n",
       "enttypeminu_COUNT          0.549604\n",
       "enttypegb_COUNT            0.411440\n",
       "label                      1.000000\n",
       "public_MAX                 0.363826\n",
       "public_MIN                 0.205591\n",
       "public_MEAN                0.368823\n",
       "changeinfo_bgrq_NUNIQUE    0.232512\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_data.corr()['label'].abs()[train_data.corr()['label'].abs() > 0.2]"
   ]
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  {
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   "execution_count": 716,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T18:45:25.401097Z",
     "start_time": "2020-11-19T18:45:25.389171Z"
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   "execution_count": 17,
   "metadata": {
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     "end_time": "2020-11-21T02:48:17.973232Z",
     "start_time": "2020-11-21T02:48:17.915088Z"
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    "scrolled": true
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  {
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   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-21T02:47:12.228302Z",
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       "    <tr>\n",
       "      <th>24862</th>\n",
       "      <td>da8691b210adb3f65b43370d3a362f4aa1d3b16b5ba0c9d7</td>\n",
       "      <td>340207</td>\n",
       "      <td>O</td>\n",
       "      <td>8111.0</td>\n",
       "      <td>ca213febe80e171c3b9617e39b49d64e4a4abca4f2d760...</td>\n",
       "      <td>快速喷漆、电路机维修、汽车美容装潢、汽车维修服务。（依法须经批准的项目，经相关部门批准后方可...</td>\n",
       "      <td>9600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-05-31 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>340207030010000000</td>\n",
       "      <td>340200000000115275</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2367b4cac96d8598</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24863</th>\n",
       "      <td>516ab81418ed215dcbbf0614a7b929e691f8eed153d7bb31</td>\n",
       "      <td>340225</td>\n",
       "      <td>O</td>\n",
       "      <td>8090.0</td>\n",
       "      <td>31487d8f256f16bd6244b7251be2ebb227ca54d864983f...</td>\n",
       "      <td>一般经营项目：园林设计、市政绿化、假山工艺、苗圃栽培。</td>\n",
       "      <td>1100</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>2012-10-16 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>340200000000116750</td>\n",
       "      <td>341400000000015220</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2367b4cac96d8598</td>\n",
       "      <td>260.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24864</th>\n",
       "      <td>9c7fa510616a68303d3427d4bfd4b0cf3e4843f2bf3f637a</td>\n",
       "      <td>340222</td>\n",
       "      <td>N</td>\n",
       "      <td>7830.0</td>\n",
       "      <td>31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...</td>\n",
       "      <td>各类广告设计、制作，不锈钢制品制作及销售（依法须经批准的项目，经相关部门批准后方可开展经营活动）</td>\n",
       "      <td>9600</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>2011-05-27 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>340222070010000000</td>\n",
       "      <td>340200000020003395</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24865 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     id  oplocdistrict  \\\n",
       "0      47645761dc56bb8c5fae00114b768b5d9b6e917c3aec07c4         340223   \n",
       "1      9c7fa510616a683058ce97d0bc768a621cd85ab1e87da2a3         340222   \n",
       "2      59b38c56de3836838082cfcb1a298951abfe15e6940c49ba         340202   \n",
       "3      e9f7b28ec10e047000d16ab79e1b5e6da434a1697cce7818         340221   \n",
       "4      f000950527a6feb63ee1ce82bb22ddd1ab8b8fdffa3b91fb         340202   \n",
       "...                                                 ...            ...   \n",
       "24860  f1c1045b13d18329a2bd99d2a7e2227688c0d69bf1d1e325         340225   \n",
       "24861  f000950527a6feb6bde38216d7cbbf32e66d3a3a96d4dbda         340207   \n",
       "24862  da8691b210adb3f65b43370d3a362f4aa1d3b16b5ba0c9d7         340207   \n",
       "24863  516ab81418ed215dcbbf0614a7b929e691f8eed153d7bb31         340225   \n",
       "24864  9c7fa510616a68303d3427d4bfd4b0cf3e4843f2bf3f637a         340222   \n",
       "\n",
       "      industryphy  industryco  \\\n",
       "0               M      7513.0   \n",
       "1               O      8090.0   \n",
       "2               R      9053.0   \n",
       "3               L      7212.0   \n",
       "4               R      8810.0   \n",
       "...           ...         ...   \n",
       "24860           O      8131.0   \n",
       "24861           J      6790.0   \n",
       "24862           O      8111.0   \n",
       "24863           O      8090.0   \n",
       "24864           N      7830.0   \n",
       "\n",
       "                                                     dom  \\\n",
       "0      31487d8f256f16bd6244b7251be2ebb24d1db51663c654...   \n",
       "1      31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...   \n",
       "2      31487d8f256f16bd6244b7251be2ebb2ae36cd652943e8...   \n",
       "3      746df9aaed8578571760c563abe882c8ba25209fc6d5db...   \n",
       "4      31487d8f256f16bd6244b7251be2ebb2ae36cd652943e8...   \n",
       "...                                                  ...   \n",
       "24860  31487d8f256f16bd6244b7251be2ebb227ca54d864983f...   \n",
       "24861  31487d8f256f16bdb06579d53b25cbb6c4a6c18662c495...   \n",
       "24862  ca213febe80e171c3b9617e39b49d64e4a4abca4f2d760...   \n",
       "24863  31487d8f256f16bd6244b7251be2ebb227ca54d864983f...   \n",
       "24864  31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...   \n",
       "\n",
       "                                                 opscope  enttype  \\\n",
       "0      纳米新材料、机械设备、五金配件加工、销售及技术推广服务，道路货物运输。（依法须经批准的项目，...     1100   \n",
       "1                      健身服务。（依法须经批准的项目，经相关部门批准后方可开展经营活动）     9600   \n",
       "2      文化娱乐经纪人服务；境内文艺活动组织与策划；文化艺术交流活动组织策划；演出经纪；其他文化艺术...     1100   \n",
       "3      投资管理及咨询(证券、期货除外)；企业管理。（依法须经批准的项目，经相关部门批准后方可开展经...     4500   \n",
       "4      境内文化艺术交流活动策划；企业形象策划；礼仪庆典服务；翻译服务；专利代理；广告设计、制作、代...     1100   \n",
       "...                                                  ...      ...   \n",
       "24860          家电销售、维修及安装服务。（依法须经批准的项目，经相关部门批准后方可开展经营活动）     9600   \n",
       "24861     股权投资，投资管理，投资咨询。（依法须经批准的项目，经相关部门批准后方可开展经营活动）***     4500   \n",
       "24862  快速喷漆、电路机维修、汽车美容装潢、汽车维修服务。（依法须经批准的项目，经相关部门批准后方可...     9600   \n",
       "24863                        一般经营项目：园林设计、市政绿化、假山工艺、苗圃栽培。     1100   \n",
       "24864   各类广告设计、制作，不锈钢制品制作及销售（依法须经批准的项目，经相关部门批准后方可开展经营活动）     9600   \n",
       "\n",
       "       enttypeitem               opfrom        opto  state  \\\n",
       "0           1150.0  2019-07-11 00:00:00         NaN      6   \n",
       "1              NaN           2017-09-06         NaN      6   \n",
       "2           1150.0  2020-09-14 14:46:30         NaN      6   \n",
       "3           4540.0           2015-09-30         NaN      6   \n",
       "4           1130.0           2017-12-01  2067-11-30      7   \n",
       "...            ...                  ...         ...    ...   \n",
       "24860          NaN  2009-02-20 00:00:00         NaN      6   \n",
       "24861       4530.0           2015-12-18  2065-12-17      6   \n",
       "24862          NaN  2012-05-31 00:00:00         NaN      6   \n",
       "24863       1130.0  2012-10-16 00:00:00         NaN      7   \n",
       "24864       9600.0  2011-05-27 00:00:00         NaN      6   \n",
       "\n",
       "                    orgid               jobid  adbusign  townsign  regtype  \\\n",
       "0      340223010010000000  340200000000115392         0         0        1   \n",
       "1      340222060010000000  340200000000112114         0         1        1   \n",
       "2      340202010010000000  400000000000753910         0         0        1   \n",
       "3      340221010010000000  400000000000013538         0         1        1   \n",
       "4      340200000000000000  400000000000283237         0         0        1   \n",
       "...                   ...                 ...       ...       ...      ...   \n",
       "24860  340200000000116780  341400000000011622         0         1        1   \n",
       "24861  340200000000000000  340200000000115797         0         1        1   \n",
       "24862  340207030010000000  340200000000115275         0         1        1   \n",
       "24863  340200000000116750  341400000000015220         0         1        1   \n",
       "24864  340222070010000000  340200000020003395         0         1        1   \n",
       "\n",
       "       empnum  compform  parnum  exenum      opform  ptbusscope  venind  \\\n",
       "0         5.0       NaN     NaN     NaN         NaN         NaN     NaN   \n",
       "1         3.0       1.0     NaN     NaN          10         NaN     3.0   \n",
       "2         2.0       NaN     1.0     NaN         NaN         NaN     NaN   \n",
       "3         2.0       NaN     NaN     NaN  01-以个人财产出资         NaN     NaN   \n",
       "4         NaN       NaN     NaN     NaN         NaN         NaN     NaN   \n",
       "...       ...       ...     ...     ...         ...         ...     ...   \n",
       "24860     3.0       1.0     NaN     NaN          10         NaN     3.0   \n",
       "24861     2.0       NaN     2.0     1.0         NaN         NaN     NaN   \n",
       "24862     2.0       1.0     NaN     NaN          10         NaN     3.0   \n",
       "24863     3.0       1.0     NaN     NaN         NaN         NaN     3.0   \n",
       "24864     1.0       1.0     NaN     NaN          10         NaN     3.0   \n",
       "\n",
       "       enttypeminu  midpreindcode  protype  \\\n",
       "0           1151.0            NaN      NaN   \n",
       "1              NaN            NaN      NaN   \n",
       "2           1151.0            NaN      NaN   \n",
       "3              NaN            NaN      NaN   \n",
       "4              NaN            NaN      NaN   \n",
       "...            ...            ...      ...   \n",
       "24860          NaN            NaN      NaN   \n",
       "24861       4533.0            NaN      NaN   \n",
       "24862          NaN            NaN      NaN   \n",
       "24863          NaN            NaN      NaN   \n",
       "24864          NaN            NaN      NaN   \n",
       "\n",
       "                                                   oploc  regcap  reccap  \\\n",
       "0                                       2367b4cac96d8598    50.0     NaN   \n",
       "1      31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...    10.0     NaN   \n",
       "2                                       2367b4cac96d8598   100.0     NaN   \n",
       "3                                       2367b4cac96d8598    10.0     NaN   \n",
       "4                                       2367b4cac96d8598   100.0     NaN   \n",
       "...                                                  ...     ...     ...   \n",
       "24860  f67c1b92f52ac52e424308ab51241cdef9be3e39c8e1c6...    20.0     NaN   \n",
       "24861                                   2367b4cac96d8598   110.0     0.0   \n",
       "24862                                   2367b4cac96d8598    10.0     NaN   \n",
       "24863                                   2367b4cac96d8598   260.0   260.0   \n",
       "24864  31487d8f256f16bd6244b7251be2ebb27b17bdfd95c8f3...    10.0     NaN   \n",
       "\n",
       "       forreccap  forregcap  congro  enttypegb  \n",
       "0            NaN        NaN     NaN       1151  \n",
       "1            NaN        NaN     NaN       9600  \n",
       "2            NaN        NaN     NaN       1151  \n",
       "3            NaN        NaN     NaN       4540  \n",
       "4            NaN        NaN     NaN       1130  \n",
       "...          ...        ...     ...        ...  \n",
       "24860        NaN        NaN     NaN       9600  \n",
       "24861        NaN        NaN     NaN       4533  \n",
       "24862        NaN        NaN     NaN       9600  \n",
       "24863        NaN        NaN     NaN       1130  \n",
       "24864        NaN        NaN     NaN       9600  \n",
       "\n",
       "[24865 rows x 33 columns]"
      ]
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